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<div class="title">rf_face_utils.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * fanellis_face_detector.h</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Created on: 22 Sep 2012</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *      Author: Aitor Aldoma</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160; </div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#ifndef PCL_RF_FACE_UTILS_H_</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#define PCL_RF_FACE_UTILS_H_</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160; </div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;pcl/recognition/face_detection/face_common.h&quot;</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &lt;pcl/ml/feature_handler.h&gt;</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &lt;pcl/ml/stats_estimator.h&gt;</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &lt;pcl/ml/branch_estimator.h&gt;</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160; </div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="keyword">namespace </span>pcl</div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;{</div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;  <span class="keyword">namespace </span>face_detection</div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;  {</div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> FT, <span class="keyword">class</span> DataSet, <span class="keyword">class</span> ExampleIndex&gt;</div>
<div class="line"><a name="l00021"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html">   21</a></span>&#160;    <span class="keyword">class </span><a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html">FeatureHandlerDepthAverage</a>: <span class="keyword">public</span> <a class="code" href="classpcl_1_1_feature_handler.html">pcl::FeatureHandler</a>&lt;FT, DataSet, ExampleIndex&gt;</div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;    {</div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160; </div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;      <span class="keyword">private</span>:</div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;        <span class="keywordtype">int</span> wsize_; <span class="comment">//size of the window</span></div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;        <span class="keywordtype">int</span> max_patch_size_; <span class="comment">//max size of the smaller patches</span></div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;        <span class="keywordtype">int</span> num_channels_; <span class="comment">//the number of feature channels</span></div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;        <span class="keywordtype">float</span> min_valid_small_patch_depth_; <span class="comment">//percentage of valid depth in a small patch</span></div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;      <span class="keyword">public</span>:</div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160; </div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;        <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html">FeatureHandlerDepthAverage</a>()</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;        {</div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;          wsize_ = 80;</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;          max_patch_size_ = 40;</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;          num_channels_ = 1;</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;          min_valid_small_patch_depth_ = 0.5f;</div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;        }</div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160; </div>
<div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a063ea354c83374ef8dd4167e09bf3a0a">   42</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a063ea354c83374ef8dd4167e09bf3a0a">setWSize</a>(<span class="keywordtype">int</span> w)</div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;        {</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;          wsize_ = w;</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;        }</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160; </div>
<div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#ac9eb8bc9d8dbfe04e7d12fce2495359d">   50</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#ac9eb8bc9d8dbfe04e7d12fce2495359d">setNumChannels</a>(<span class="keywordtype">int</span> nf)</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;        {</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;          num_channels_ = nf;</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;        }</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160; </div>
<div class="line"><a name="l00058"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a1534caca4512eda3d0ce165f62ef4cf9">   58</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a1534caca4512eda3d0ce165f62ef4cf9">setMaxPatchSize</a>(<span class="keywordtype">int</span> w)</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;        {</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;          max_patch_size_ = w;</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;        }</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160; </div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;        <span class="comment">/*void createRandomFeatures(const size_t num_of_features, std::vector&lt;FT&gt; &amp; features)</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="comment">         {</span></div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="comment">         srand (time(NULL));</span></div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;<span class="comment">         int min_s = 10;</span></div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;<span class="comment">         float range_d = 0.03f;</span></div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;<span class="comment">         for (size_t i = 0; i &lt; num_of_features; i++)</span></div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;<span class="comment">         {</span></div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;<span class="comment">         FT f;</span></div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;<span class="comment"></span> </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;<span class="comment">         f.row1_ = rand () % (wsize_ - max_patch_size_ - 1);</span></div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;<span class="comment">         f.col1_ = rand () % (wsize_ / 2 - max_patch_size_ - 1);</span></div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;<span class="comment">         f.wsizex1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));</span></div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;<span class="comment">         f.wsizey1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));</span></div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;<span class="comment"></span> </div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;<span class="comment">         f.row2_ = rand () % (wsize_ - max_patch_size_ - 1);</span></div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;<span class="comment">         f.col2_ = wsize_ / 2 + rand () % (wsize_ / 2 - max_patch_size_ - 1);</span></div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;<span class="comment">         f.wsizex2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));</span></div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;<span class="comment">         f.wsizey2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));</span></div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;<span class="comment"></span> </div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;<span class="comment">         f.used_ii_ = 0;</span></div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;<span class="comment">         if(num_channels_ &gt; 1)</span></div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;<span class="comment">         f.used_ii_ = rand() % num_channels_;</span></div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;<span class="comment"></span> </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;<span class="comment">         f.threshold_ = -range_d + (rand () / static_cast&lt;float&gt; (RAND_MAX)) * (range_d * 2.f);</span></div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;<span class="comment">         features.push_back (f);</span></div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;<span class="comment">         }</span></div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;<span class="comment">         }*/</span></div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00095"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a4d139c5099b1f0b68961cf54aa109dad">   95</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a4d139c5099b1f0b68961cf54aa109dad">createRandomFeatures</a>(<span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_features, std::vector&lt;FT&gt; &amp; features)</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        {</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;          srand (<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span>(time (NULL)));</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;          <span class="keywordtype">int</span> min_s = 20;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;          <span class="keywordtype">float</span> range_d = 0.05f;</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;          <span class="keywordtype">float</span> incr_d = 0.01f;</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160; </div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;          std::vector &lt; FT &gt; windows_and_functions;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160; </div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; num_of_features; i++)</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;          {</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;            FT f;</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;            f.row1_ = rand () % (wsize_ - max_patch_size_ - 1);</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;            f.col1_ = rand () % (wsize_ / 2 - max_patch_size_ - 1);</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;            f.wsizex1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;            f.wsizey1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160; </div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;            f.row2_ = rand () % (wsize_ - max_patch_size_ - 1);</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;            f.col2_ = wsize_ / 2 + rand () % (wsize_ / 2 - max_patch_size_ - 1);</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;            f.wsizex2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;            f.wsizey2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160; </div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;            f.used_ii_ = 0;</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;            <span class="keywordflow">if</span> (num_channels_ &gt; 1)</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;              f.used_ii_ = rand () % num_channels_;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160; </div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;            windows_and_functions.push_back (f);</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;          }</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160; </div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; windows_and_functions.size (); i++)</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;          {</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;            FT f = windows_and_functions[i];</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt;= 10; j++)</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;            {</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;              f.threshold_ = -range_d + <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (j) * incr_d;</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;              features.push_back (f);</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;            }</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;          }</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        }</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160; </div>
<div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a64aa9273710c173d8ed7214fcdce91d6">  143</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a64aa9273710c173d8ed7214fcdce91d6">evaluateFeature</a>(<span class="keyword">const</span> FT &amp; feature, DataSet &amp; data_set, std::vector&lt;ExampleIndex&gt; &amp; examples, std::vector&lt;float&gt; &amp; results,</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;            std::vector&lt;unsigned char&gt; &amp; flags)<span class="keyword"> const</span></div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;          results.resize (examples.size ());</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; examples.size (); i++)</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;          {</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a64aa9273710c173d8ed7214fcdce91d6">evaluateFeature</a> (feature, data_set, examples[i], results[i], flags[i]);</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;          }</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;        }</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
<div class="line"><a name="l00160"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a84f08ec6e223bcd3db975cf2c597d9e9">  160</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a84f08ec6e223bcd3db975cf2c597d9e9">evaluateFeature</a>(<span class="keyword">const</span> FT &amp; feature, DataSet &amp; data_set, <span class="keyword">const</span> ExampleIndex &amp; example, <span class="keywordtype">float</span> &amp; result, <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> &amp; flag)<span class="keyword"> const</span></div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;          <a class="code" href="classpcl_1_1face__detection_1_1_training_example.html">TrainingExample</a> te = data_set[example];</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;          <span class="keywordtype">int</span> el_f1 = te.iimages_[feature.used_ii_]-&gt;getFiniteElementsCount (te.col_ + feature.col1_, te.row_ + feature.row1_, feature.wsizex1_,</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;              feature.wsizey1_);</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;          <span class="keywordtype">int</span> el_f2 = te.iimages_[feature.used_ii_]-&gt;getFiniteElementsCount (te.col_ + feature.col2_, te.row_ + feature.row2_, feature.wsizex2_,</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;              feature.wsizey2_);</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160; </div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;          <span class="keywordtype">float</span> sum_f1 = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(te.iimages_[feature.used_ii_]-&gt;getFirstOrderSum (te.col_ + feature.col1_, te.row_ + feature.row1_, feature.wsizex1_, feature.wsizey1_));</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;          <span class="keywordtype">float</span> sum_f2 = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(te.iimages_[feature.used_ii_]-&gt;getFirstOrderSum (te.col_ + feature.col2_, te.row_ + feature.row2_, feature.wsizex2_, feature.wsizey2_));</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; </div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;          <span class="keywordtype">float</span> f = min_valid_small_patch_depth_;</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;          <span class="keywordflow">if</span> (el_f1 == 0 || el_f2 == 0 || (el_f1 &lt;= <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (f * <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(feature.wsizex1_ * feature.wsizey1_)))</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;              || (el_f2 &lt;= <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (f * <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(feature.wsizex2_ * feature.wsizey2_))))</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;          {</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;            result = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (pcl_round (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(rand ()) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (RAND_MAX)));</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;            flag = 1;</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;          } <span class="keywordflow">else</span></div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;          {</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;            result = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> ((sum_f1 / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(el_f1) - sum_f2 / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(el_f2)) &gt; feature.threshold_);</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;            flag = 0;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;          }</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160; </div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        }</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160; </div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;         <span class="comment">// param[in] feature The feature for which code is generated.</span></div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;         <span class="comment">// param[out] stream The destination for the code.</span></div>
<div class="line"><a name="l00189"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a8a093bec62b9ad8bc70343a9807605a2">  189</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a8a093bec62b9ad8bc70343a9807605a2">generateCodeForEvaluation</a>(<span class="keyword">const</span> FT &amp;<span class="comment">/*feature*/</span>, ::std::ostream &amp;<span class="comment">/*stream*/</span>)<span class="keyword"> const</span></div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160; </div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        }</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    };</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160; </div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    <span class="keyword">template</span>&lt;<span class="keyword">class</span> LabelDataType, <span class="keyword">class</span> NodeType, <span class="keyword">class</span> DataSet, <span class="keyword">class</span> ExampleIndex&gt;</div>
<div class="line"><a name="l00197"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">  197</a></span>&#160;    <span class="keyword">class </span><a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">PoseClassRegressionVarianceStatsEstimator</a>: <span class="keyword">public</span> <a class="code" href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a>&lt;LabelDataType, NodeType, DataSet, ExampleIndex&gt;</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    {</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160; </div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      <span class="keyword">public</span>:</div>
<div class="line"><a name="l00202"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a437ef68ac62fe7af67c98d69585dcdc7">  202</a></span>&#160;        <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a437ef68ac62fe7af67c98d69585dcdc7">PoseClassRegressionVarianceStatsEstimator</a>(<a class="code" href="classpcl_1_1_branch_estimator.html">BranchEstimator</a> * branch_estimator) :</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">branch_estimator_</a> (branch_estimator)</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        {</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;        }</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160; </div>
<div class="line"><a name="l00208"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aba50205ec09702af045e369a0d85d849">  208</a></span>&#160;        <span class="keyword">virtual</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aba50205ec09702af045e369a0d85d849">~PoseClassRegressionVarianceStatsEstimator</a>()</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        {</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        }</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160; </div>
<div class="line"><a name="l00213"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a33a428f0478566c3b75f2d14c28039b7">  213</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">size_t</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a33a428f0478566c3b75f2d14c28039b7">getNumOfBranches</a>()<span class="keyword"> const</span></div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;          <span class="keywordflow">return</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">branch_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_branch_estimator.html#a5d61cf26e1fcb520efa1dada6f5c07ba">getNumOfBranches</a> ();</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        }</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160; </div>
<div class="line"><a name="l00221"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#af483f527511dd87173ff5c4dd130340e">  221</a></span>&#160;        <span class="keyword">inline</span> LabelDataType <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#af483f527511dd87173ff5c4dd130340e">getLabelOfNode</a>(NodeType &amp; node)<span class="keyword"> const</span></div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;          <span class="keywordflow">return</span> node.value;</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        }</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160; </div>
<div class="line"><a name="l00232"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">  232</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">computeMeanAndCovarianceOffset</a>(DataSet &amp; data_set, std::vector&lt;ExampleIndex&gt; &amp; examples, Eigen::Matrix3d &amp; covariance_matrix,</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;            Eigen::Vector3d &amp; centroid)<span class="keyword"> const</span></div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;          Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt; accu = Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt;::Zero ();</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;          <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> point_count = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span> (examples.size ());</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; point_count; ++i)</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;          {</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_training_example.html">TrainingExample</a> te = data_set[examples[i]];</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;            accu[0] += te.trans_[0] * te.trans_[0];</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;            accu[1] += te.trans_[0] * te.trans_[1];</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;            accu[2] += te.trans_[0] * te.trans_[2];</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;            accu[3] += te.trans_[1] * te.trans_[1];</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;            accu[4] += te.trans_[1] * te.trans_[2];</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;            accu[5] += te.trans_[2] * te.trans_[2];</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;            accu[6] += te.trans_[0];</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;            accu[7] += te.trans_[1];</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;            accu[8] += te.trans_[2];</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;          }</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160; </div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;          <span class="keywordflow">if</span> (point_count != 0)</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;          {</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;            accu /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span> (point_count);</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;            centroid.head&lt;3&gt; ().matrix () = accu.tail&lt;3&gt; ();</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;            covariance_matrix.coeffRef (0) = accu[0] - accu[6] * accu[6];</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;            covariance_matrix.coeffRef (1) = accu[1] - accu[6] * accu[7];</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;            covariance_matrix.coeffRef (2) = accu[2] - accu[6] * accu[8];</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;            covariance_matrix.coeffRef (4) = accu[3] - accu[7] * accu[7];</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;            covariance_matrix.coeffRef (5) = accu[4] - accu[7] * accu[8];</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;            covariance_matrix.coeffRef (8) = accu[5] - accu[8] * accu[8];</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;            covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;            covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;            covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;          }</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160; </div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;          <span class="keywordflow">return</span> point_count;</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        }</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160; </div>
<div class="line"><a name="l00276"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">  276</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">computeMeanAndCovarianceAngles</a>(DataSet &amp; data_set, std::vector&lt;ExampleIndex&gt; &amp; examples, Eigen::Matrix3d &amp; covariance_matrix,</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;            Eigen::Vector3d &amp; centroid)<span class="keyword"> const</span></div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;          Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt; accu = Eigen::Matrix&lt;double, 1, 9, Eigen::RowMajor&gt;::Zero ();</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;          <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> point_count = <span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span> <span class="keywordtype">int</span><span class="keyword">&gt;</span> (examples.size ());</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160; </div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; point_count; ++i)</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;          {</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_training_example.html">TrainingExample</a> te = data_set[examples[i]];</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;            accu[0] += te.rot_[0] * te.rot_[0];</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;            accu[1] += te.rot_[0] * te.rot_[1];</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;            accu[2] += te.rot_[0] * te.rot_[2];</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;            accu[3] += te.rot_[1] * te.rot_[1];</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;            accu[4] += te.rot_[1] * te.rot_[2];</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;            accu[5] += te.rot_[2] * te.rot_[2];</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;            accu[6] += te.rot_[0];</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;            accu[7] += te.rot_[1];</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;            accu[8] += te.rot_[2];</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;          }</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160; </div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;          <span class="keywordflow">if</span> (point_count != 0)</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;          {</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;            accu /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span> (point_count);</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;            centroid.head&lt;3&gt; ().matrix () = accu.tail&lt;3&gt; ();</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;            covariance_matrix.coeffRef (0) = accu[0] - accu[6] * accu[6];</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;            covariance_matrix.coeffRef (1) = accu[1] - accu[6] * accu[7];</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;            covariance_matrix.coeffRef (2) = accu[2] - accu[6] * accu[8];</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;            covariance_matrix.coeffRef (4) = accu[3] - accu[7] * accu[7];</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;            covariance_matrix.coeffRef (5) = accu[4] - accu[7] * accu[8];</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;            covariance_matrix.coeffRef (8) = accu[5] - accu[8] * accu[8];</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;            covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;            covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;            covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;          }</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160; </div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;          <span class="keywordflow">return</span> point_count;</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;        }</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160; </div>
<div class="line"><a name="l00322"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a99c1a899a7a6336508dc54b4d2445229">  322</a></span>&#160;        <span class="keywordtype">float</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a99c1a899a7a6336508dc54b4d2445229">computeInformationGain</a>(DataSet &amp; data_set, std::vector&lt;ExampleIndex&gt; &amp; examples, std::vector&lt;LabelDataType&gt; &amp; label_data,</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;            std::vector&lt;float&gt; &amp; results, std::vector&lt;unsigned char&gt; &amp; flags, <span class="keyword">const</span> <span class="keywordtype">float</span> threshold)<span class="keyword"> const</span></div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples = examples.size ();</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_branches = <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a33a428f0478566c3b75f2d14c28039b7">getNumOfBranches</a> ();</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160; </div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;          <span class="comment">// compute variance</span></div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;          std::vector &lt; LabelDataType &gt; sums (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;          std::vector &lt; LabelDataType &gt; sqr_sums (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;          std::vector &lt; size_t &gt; branch_element_count (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160; </div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; num_of_branches; ++branch_index)</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;          {</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;            branch_element_count[branch_index] = 1;</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;            ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;          }</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160; </div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;          {</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a> (results[example_index], flags[example_index], threshold, branch_index);</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160; </div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;            LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160; </div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;            ++branch_element_count[branch_index];</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;            ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160; </div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;            sums[branch_index] += label;</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;            sums[num_of_branches] += label;</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;          }</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160; </div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;          std::vector&lt;float&gt; hp (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;          {</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;            <span class="keywordtype">float</span> pf = sums[branch_index] / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[branch_index]);</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;            <span class="keywordtype">float</span> pnf = (<span class="keyword">static_cast&lt;</span>LabelDataType<span class="keyword">&gt;</span>(branch_element_count[branch_index]) - sums[branch_index] + 1.f)</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;                        / <span class="keyword">static_cast&lt;</span>LabelDataType<span class="keyword">&gt;</span> (branch_element_count[branch_index]);</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;            hp[branch_index] -= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(pf * log (pf) + pnf * log (pnf));</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;          }</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160; </div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;          <span class="comment">//use mean of the examples as purity</span></div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;          <span class="keywordtype">float</span> purity = sums[num_of_branches] / <span class="keyword">static_cast&lt;</span>LabelDataType<span class="keyword">&gt;</span>(branch_element_count[num_of_branches]);</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;          <span class="keywordtype">float</span> tp = 0.8f;</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160; </div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;          <span class="keywordflow">if</span> (purity &gt;= tp)</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;          {</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;            <span class="comment">//compute covariance matrices from translation offsets and angles for the whole set and children</span></div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;            <span class="comment">//consider only positive examples...</span></div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;            std::vector &lt; size_t &gt; branch_element_count (num_of_branches + 1, 0);</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;            std::vector &lt; std::vector&lt;ExampleIndex&gt; &gt; positive_examples;</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;            positive_examples.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160; </div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;            <span class="keywordtype">size_t</span> pos = 0;</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;            {</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;              <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;              <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a> (results[example_index], flags[example_index], threshold, branch_index);</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160; </div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;              LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160; </div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;              <span class="keywordflow">if</span> (label == 1 <span class="comment">/*&amp;&amp; !flags[example_index]*/</span>)</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;              {</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;                ++branch_element_count[branch_index];</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;                ++branch_element_count[num_of_branches];</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160; </div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;                positive_examples[branch_index].push_back (examples[example_index]);</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;                positive_examples[num_of_branches].push_back (examples[example_index]);</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;                pos++;</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;              }</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;            }</div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160; </div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;            <span class="comment">//compute covariance from offsets and angles for all branchs</span></div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;            std::vector &lt; Eigen::Matrix3d &gt; offset_covariances;</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;            std::vector &lt; Eigen::Matrix3d &gt; angle_covariances;</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160; </div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;            std::vector &lt; Eigen::Vector3d &gt; offset_centroids;</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;            std::vector &lt; Eigen::Vector3d &gt; angle_centroids;</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160; </div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;            offset_covariances.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;            angle_covariances.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;            offset_centroids.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;            angle_centroids.resize (num_of_branches + 1);</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160; </div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;            {</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;              <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">computeMeanAndCovarianceOffset</a> (data_set, positive_examples[branch_index], offset_covariances[branch_index],</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;                  offset_centroids[branch_index]);</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;              <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">computeMeanAndCovarianceAngles</a> (data_set, positive_examples[branch_index], angle_covariances[branch_index],</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;                  angle_centroids[branch_index]);</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;            }</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160; </div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;            <span class="comment">//update information_gain</span></div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;            std::vector&lt;float&gt; hr (num_of_branches + 1, 0.f);</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;            {</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;              hr[branch_index] = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(0.5f * log (std::pow (2 * M_PI, 3)</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;                                                    * offset_covariances[branch_index].determinant ())</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;                                                    + 0.5f * log (std::pow (2 * M_PI, 3)</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;                                                    * angle_covariances[branch_index].determinant ()));</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;            }</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160; </div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches + 1); ++branch_index)</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;            {</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;              hp[branch_index] += std::max (sums[branch_index] / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[branch_index]) - tp, 0.f) * hr[branch_index];</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;            }</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;          }</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160; </div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;          <span class="keywordtype">float</span> information_gain = hp[num_of_branches + 1];</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> branch_index = 0; branch_index &lt; (num_of_branches); ++branch_index)</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;          {</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;            information_gain -= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[branch_index]) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (branch_element_count[num_of_branches])</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;                * hp[branch_index];</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160;          }</div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160; </div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;          <span class="keywordflow">return</span> information_gain;</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;        }</div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160; </div>
<div class="line"><a name="l00445"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a4bd8fe3020b0adb041b68bcb01477dd8">  445</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a4bd8fe3020b0adb041b68bcb01477dd8">computeBranchIndices</a>(std::vector&lt;float&gt; &amp; results, std::vector&lt;unsigned char&gt; &amp; flags, <span class="keyword">const</span> <span class="keywordtype">float</span> threshold,</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;            std::vector&lt;unsigned char&gt; &amp; branch_indices)<span class="keyword"> const</span></div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_results = results.size ();</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160; </div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;          branch_indices.resize (num_of_results);</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> result_index = 0; result_index &lt; num_of_results; ++result_index)</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;          {</div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;            <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> branch_index;</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;            <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a> (results[result_index], flags[result_index], threshold, branch_index);</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;            branch_indices[result_index] = branch_index;</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;          }</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;        }</div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160; </div>
<div class="line"><a name="l00465"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">  465</a></span>&#160;        <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">computeBranchIndex</a>(<span class="keyword">const</span> <span class="keywordtype">float</span> result, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> flag, <span class="keyword">const</span> <span class="keywordtype">float</span> threshold, <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span> &amp; branch_index)<span class="keyword"> const</span></div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160;          <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">branch_estimator_</a>-&gt;<a class="code" href="classpcl_1_1_branch_estimator.html#a595a4e2ddc742910336912ff66c6feba">computeBranchIndex</a> (result, flag, threshold, branch_index);</div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;        }</div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160; </div>
<div class="line"><a name="l00476"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aa27bf41909f401c9aaaea96693eba5fb">  476</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aa27bf41909f401c9aaaea96693eba5fb">computeAndSetNodeStats</a>(DataSet &amp; data_set, std::vector&lt;ExampleIndex&gt; &amp; examples, std::vector&lt;LabelDataType&gt; &amp; label_data, NodeType &amp; node)<span class="keyword"> const</span></div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_of_examples = examples.size ();</div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160; </div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160;          LabelDataType sum = 0.0f;</div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;          LabelDataType sqr_sum = 0.0f;</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;          {</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;            <span class="keyword">const</span> LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160; </div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160;            sum += label;</div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;            sqr_sum += label * label;</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;          }</div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160; </div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;          sum /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(num_of_examples);</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;          sqr_sum /= <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>(num_of_examples);</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160; </div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;          <span class="keyword">const</span> <span class="keywordtype">float</span> variance = sqr_sum - sum * sum;</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160; </div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;          node.value = sum;</div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160;          node.variance = variance;</div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160; </div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;          <span class="comment">//set node stats regarding pose regression</span></div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;          std::vector &lt; ExampleIndex &gt; positive_examples;</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160; </div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> example_index = 0; example_index &lt; num_of_examples; ++example_index)</div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;          {</div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;            LabelDataType label = label_data[example_index];</div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160; </div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;            <span class="keywordflow">if</span> (label == 1)</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;              positive_examples.push_back (examples[example_index]);</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160; </div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160;          }</div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160; </div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;          <span class="comment">//compute covariance from offsets and angles</span></div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;          <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">computeMeanAndCovarianceOffset</a> (data_set, positive_examples, node.covariance_trans_, node.trans_mean_);</div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;          <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">computeMeanAndCovarianceAngles</a> (data_set, positive_examples, node.covariance_rot_, node.rot_mean_);</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;        }</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160; </div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;        <span class="comment">// param[in] node The node for which code is generated.</span></div>
<div class="line"><a name="l00519"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a965ac0738f571aae26a3f52ccbf26f8f">  519</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a965ac0738f571aae26a3f52ccbf26f8f">generateCodeForBranchIndexComputation</a>(NodeType &amp; <span class="comment">/*node*/</span>, std::ostream &amp; stream)<span class="keyword"> const</span></div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;          stream &lt;&lt; <span class="stringliteral">&quot;ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)&quot;</span>;</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        }</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160; </div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;        <span class="comment">// param[in] node The node for which code is generated.</span></div>
<div class="line"><a name="l00528"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a387f6dafb7ca51c20793fab106b202e1">  528</a></span>&#160;        <span class="keywordtype">void</span> <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a387f6dafb7ca51c20793fab106b202e1">generateCodeForOutput</a>(NodeType &amp; <span class="comment">/*node*/</span>, std::ostream &amp; stream)<span class="keyword"> const</span></div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;<span class="keyword">        </span>{</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;          stream &lt;&lt; <span class="stringliteral">&quot;ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)&quot;</span>;</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;        }</div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160; </div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;      <span class="keyword">private</span>:</div>
<div class="line"><a name="l00535"></a><span class="lineno"><a class="line" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">  535</a></span>&#160;        <a class="code" href="classpcl_1_1_branch_estimator.html">pcl::BranchEstimator</a> * <a class="code" href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">branch_estimator_</a>;</div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    };</div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;  }</div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;}</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160; </div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* PCL_RF_FACE_UTILS_H_ */</span><span class="preprocessor"></span></div>
<div class="ttc" id="aclasspcl_1_1_branch_estimator_html"><div class="ttname"><a href="classpcl_1_1_branch_estimator.html">pcl::BranchEstimator</a></div><div class="ttdoc">Interface for branch estimators.</div><div class="ttdef"><b>Definition:</b> branch_estimator.h:52</div></div>
<div class="ttc" id="aclasspcl_1_1_branch_estimator_html_a595a4e2ddc742910336912ff66c6feba"><div class="ttname"><a href="classpcl_1_1_branch_estimator.html#a595a4e2ddc742910336912ff66c6feba">pcl::BranchEstimator::computeBranchIndex</a></div><div class="ttdeci">virtual void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &amp;branch_index) const =0</div><div class="ttdoc">Computes the branch index for the specified result.</div></div>
<div class="ttc" id="aclasspcl_1_1_branch_estimator_html_a5d61cf26e1fcb520efa1dada6f5c07ba"><div class="ttname"><a href="classpcl_1_1_branch_estimator.html#a5d61cf26e1fcb520efa1dada6f5c07ba">pcl::BranchEstimator::getNumOfBranches</a></div><div class="ttdeci">virtual size_t getNumOfBranches() const =0</div><div class="ttdoc">Returns the number of branches the corresponding tree has.</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_handler_html"><div class="ttname"><a href="classpcl_1_1_feature_handler.html">pcl::FeatureHandler</a></div><div class="ttdoc">Utility class interface which is used for creating and evaluating features.</div><div class="ttdef"><b>Definition:</b> feature_handler.h:55</div></div>
<div class="ttc" id="aclasspcl_1_1_stats_estimator_html"><div class="ttname"><a href="classpcl_1_1_stats_estimator.html">pcl::StatsEstimator</a></div><div class="ttdoc">Class interface for gathering statistics for decision tree learning.</div><div class="ttdef"><b>Definition:</b> stats_estimator.h:56</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html">pcl::face_detection::FeatureHandlerDepthAverage</a></div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:22</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html_a063ea354c83374ef8dd4167e09bf3a0a"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a063ea354c83374ef8dd4167e09bf3a0a">pcl::face_detection::FeatureHandlerDepthAverage::setWSize</a></div><div class="ttdeci">void setWSize(int w)</div><div class="ttdoc">Sets the size of the window to extract features.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:42</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html_a1534caca4512eda3d0ce165f62ef4cf9"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a1534caca4512eda3d0ce165f62ef4cf9">pcl::face_detection::FeatureHandlerDepthAverage::setMaxPatchSize</a></div><div class="ttdeci">void setMaxPatchSize(int w)</div><div class="ttdoc">Create a set of random tests to evaluate examples.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:58</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html_a4d139c5099b1f0b68961cf54aa109dad"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a4d139c5099b1f0b68961cf54aa109dad">pcl::face_detection::FeatureHandlerDepthAverage::createRandomFeatures</a></div><div class="ttdeci">void createRandomFeatures(const size_t num_of_features, std::vector&lt; FT &gt; &amp;features)</div><div class="ttdoc">Create a set of random tests to evaluate examples.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:95</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html_a64aa9273710c173d8ed7214fcdce91d6"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a64aa9273710c173d8ed7214fcdce91d6">pcl::face_detection::FeatureHandlerDepthAverage::evaluateFeature</a></div><div class="ttdeci">void evaluateFeature(const FT &amp;feature, DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags) const</div><div class="ttdoc">Evaluates a feature on the specified set of examples.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:143</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html_a84f08ec6e223bcd3db975cf2c597d9e9"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a84f08ec6e223bcd3db975cf2c597d9e9">pcl::face_detection::FeatureHandlerDepthAverage::evaluateFeature</a></div><div class="ttdeci">void evaluateFeature(const FT &amp;feature, DataSet &amp;data_set, const ExampleIndex &amp;example, float &amp;result, unsigned char &amp;flag) const</div><div class="ttdoc">Evaluates a feature on the specified example.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:160</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html_a8a093bec62b9ad8bc70343a9807605a2"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#a8a093bec62b9ad8bc70343a9807605a2">pcl::face_detection::FeatureHandlerDepthAverage::generateCodeForEvaluation</a></div><div class="ttdeci">void generateCodeForEvaluation(const FT &amp;, ::std::ostream &amp;) const</div><div class="ttdoc">Generates evaluation code for the specified feature and writes it to the specified stream.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:189</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_feature_handler_depth_average_html_ac9eb8bc9d8dbfe04e7d12fce2495359d"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_feature_handler_depth_average.html#ac9eb8bc9d8dbfe04e7d12fce2495359d">pcl::face_detection::FeatureHandlerDepthAverage::setNumChannels</a></div><div class="ttdeci">void setNumChannels(int nf)</div><div class="ttdoc">Sets the number of channels a feature has (i.e. 1 - depth, 4 - depth + normals)</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:50</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator</a></div><div class="ttdoc">Statistics estimator for regression trees which optimizes information gain and pose parameters error.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:198</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a1ecad7c6e8235d22d9dded01a7b39f9e"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a1ecad7c6e8235d22d9dded01a7b39f9e">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeMeanAndCovarianceAngles</a></div><div class="ttdeci">unsigned int computeMeanAndCovarianceAngles(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, Eigen::Matrix3d &amp;covariance_matrix, Eigen::Vector3d &amp;centroid) const</div><div class="ttdoc">Computes the covariance matrix for rotation values.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:276</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a33a428f0478566c3b75f2d14c28039b7"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a33a428f0478566c3b75f2d14c28039b7">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::getNumOfBranches</a></div><div class="ttdeci">size_t getNumOfBranches() const</div><div class="ttdoc">Returns the number of branches the corresponding tree has.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:213</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a387f6dafb7ca51c20793fab106b202e1"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a387f6dafb7ca51c20793fab106b202e1">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::generateCodeForOutput</a></div><div class="ttdeci">void generateCodeForOutput(NodeType &amp;, std::ostream &amp;stream) const</div><div class="ttdoc">Generates code for label output.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:528</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a437ef68ac62fe7af67c98d69585dcdc7"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a437ef68ac62fe7af67c98d69585dcdc7">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::PoseClassRegressionVarianceStatsEstimator</a></div><div class="ttdeci">PoseClassRegressionVarianceStatsEstimator(BranchEstimator *branch_estimator)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:202</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a4bd8fe3020b0adb041b68bcb01477dd8"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a4bd8fe3020b0adb041b68bcb01477dd8">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeBranchIndices</a></div><div class="ttdeci">void computeBranchIndices(std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold, std::vector&lt; unsigned char &gt; &amp;branch_indices) const</div><div class="ttdoc">Computes the branch indices for all supplied results.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:445</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a5f5d4b1e73621420129b70c12f7b375a"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a5f5d4b1e73621420129b70c12f7b375a">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeBranchIndex</a></div><div class="ttdeci">void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &amp;branch_index) const</div><div class="ttdoc">Computes the branch index for the specified result.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:465</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a67ca30186cf7469d8cfb13f7ed71f51b"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a67ca30186cf7469d8cfb13f7ed71f51b">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeMeanAndCovarianceOffset</a></div><div class="ttdeci">unsigned int computeMeanAndCovarianceOffset(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, Eigen::Matrix3d &amp;covariance_matrix, Eigen::Vector3d &amp;centroid) const</div><div class="ttdoc">Computes the covariance matrix for translation offsets.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:232</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a6a6bd626223fb5bd6b3cb9b9d6aeea55"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a6a6bd626223fb5bd6b3cb9b9d6aeea55">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::branch_estimator_</a></div><div class="ttdeci">pcl::BranchEstimator * branch_estimator_</div><div class="ttdoc">The branch estimator.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:535</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a965ac0738f571aae26a3f52ccbf26f8f"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a965ac0738f571aae26a3f52ccbf26f8f">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::generateCodeForBranchIndexComputation</a></div><div class="ttdeci">void generateCodeForBranchIndexComputation(NodeType &amp;, std::ostream &amp;stream) const</div><div class="ttdoc">Generates code for branch index computation.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:519</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_a99c1a899a7a6336508dc54b4d2445229"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#a99c1a899a7a6336508dc54b4d2445229">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeInformationGain</a></div><div class="ttdeci">float computeInformationGain(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, std::vector&lt; float &gt; &amp;results, std::vector&lt; unsigned char &gt; &amp;flags, const float threshold) const</div><div class="ttdoc">Computes the information gain obtained by the specified threshold.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:322</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_aa27bf41909f401c9aaaea96693eba5fb"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aa27bf41909f401c9aaaea96693eba5fb">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::computeAndSetNodeStats</a></div><div class="ttdeci">void computeAndSetNodeStats(DataSet &amp;data_set, std::vector&lt; ExampleIndex &gt; &amp;examples, std::vector&lt; LabelDataType &gt; &amp;label_data, NodeType &amp;node) const</div><div class="ttdoc">Computes and sets the statistics for a node.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:476</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_aba50205ec09702af045e369a0d85d849"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#aba50205ec09702af045e369a0d85d849">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::~PoseClassRegressionVarianceStatsEstimator</a></div><div class="ttdeci">virtual ~PoseClassRegressionVarianceStatsEstimator()</div><div class="ttdoc">Destructor.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:208</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator_html_af483f527511dd87173ff5c4dd130340e"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_pose_class_regression_variance_stats_estimator.html#af483f527511dd87173ff5c4dd130340e">pcl::face_detection::PoseClassRegressionVarianceStatsEstimator::getLabelOfNode</a></div><div class="ttdeci">LabelDataType getLabelOfNode(NodeType &amp;node) const</div><div class="ttdoc">Returns the label of the specified node.</div><div class="ttdef"><b>Definition:</b> rf_face_utils.h:221</div></div>
<div class="ttc" id="aclasspcl_1_1face__detection_1_1_training_example_html"><div class="ttname"><a href="classpcl_1_1face__detection_1_1_training_example.html">pcl::face_detection::TrainingExample</a></div><div class="ttdef"><b>Definition:</b> face_common.h:11</div></div>
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